OpenRaiser/Intern-Atlas huggingface dataset:
Application of Executive Information System for COVID-19 Reporting System and Management: An Example from DKI Jakarta, Indonesia
Venue: arXiv cs.CY 2021
Year: 2021

SARS CoV-2 infection and transmission are problematic in developing countries such as Indonesia. Due to the lack of an information system, Provinces must be able to innovate in developing information systems related to surveillance of SARS CoV-2 infection. Jakarta Department of Health built a data management system called Executive Information System (EIS) of COVID-19 Reporting. EIS aimed to provide actual data so that current epidemiological analysis is accurate. The main idea of EIS is to provide valid and actual information to stakeholders, which can then be presented in the form of a dashboard. EIS is utilized to push data flow and management for rapid surveillance purposes. This could be the first time in Indonesia that a system reports near-actual data of nearly half a million people daily using an integrated system through a transparent system. The main data presented is important to monitor and evaluate COVID-19 transmission is the cumulative case dan daily case number. Data in EIS also can offer data geographically so that a more detailed analysis could be done. EIS's data and the dashboard help the government in pandemic control by presenting actual data on bed occupancy and availability across hospitals, especially isolation wards. Stakeholders, academic institutions should utilize EIS data and other elements to help Indonesia fight COVID-19.
Digital Resilience and the Continuance Use of Mobile Payment Services
Venue: arXiv cs.CY 2021
Year: 2021

The use of mobile payment services is an essential contributor to financial inclusion in emerging markets. Unfortunately, the service has become a platform for fraud. Mobile payment users need to be digitally resilient to continue using the service after adverse events. However, there is scant literature on users' continuance use of mobile payment services in the post-event of fraud. The focal point of prior literature has been on technology adoption or threat avoidance to implement policies that protect users. Analysing the relationship between individual digital resilience and post-adoption behavioural patterns will enable service providers to support individual digital resilience to promote users' continuance use of the service. This research aims to develop and empirically validate a conceptual model to examine individual digital resilience in the context of the continuance use of mobile payments. The model will be based on protection motivation theory. Survey data will be obtained from victims of mobile payment fraud and other users who continue using the service despite their knowledge of mobile payment fraud. The results from this study are expected to make key contributions to theory, practice, and policy in the areas of digital resilience, mobile payments, and ICT4D.
Bugs4Q: A Benchmark of Real Bugs for Quantum Programs
Venue: arXiv cs.SE 2021
Year: 2021

Realistic benchmarks of reproducible bugs and fixes are vital to good experimental evaluation of debugging and testing approaches. However, there is no suitable benchmark suite that can systematically evaluate the debugging and testing methods of quantum programs until now. This paper proposes Bugs4Q, a benchmark of thirty-six real, manually validated Qiskit bugs from four popular Qiskit elements (Terra, Aer, Ignis, and Aqua), supplemented with the test cases for reproducing buggy behaviors. Bugs4Q also provides interfaces for accessing the buggy and fixed versions of the Qiskit programs and executing the corresponding test cases, facilitating the reproducible empirical studies and comparisons of Qiskit program debugging and testing tools. Bugs4Q is publicly available at https://github.com/Z-928/Bugs4Q.
M-Government, Wilaya and Women's Empowerment in the Kingdom of Saudi Arabia
Venue: arXiv cs.CY 2021
Year: 2021

With the loosening of the male guardianship system (Wilaya) in the Kingdom of Saudi Arabia (KSA), this study examines the contribution of m-government to the empowerment of Saudi women. We employ the key concepts of Sen's Capability Approach (CA) to understand how m-government services in the KSA have provided opportunities for women to become empowered. The findings of the study indicate that m-government contributes towards women's empowerment by providing opportunities to participate in social and economic activities. However, there are also key social and cultural factors that impede the use of m-government services for empowerment, and we found these to include religious beliefs, Saudi traditions and customs, and husbands' jealousy. The study makes some important contributions to theory and practice by being the first study to focus on the use that Saudi women make of the opportunities now available to them to access government services through m-government applications and to address the cultural barriers which may function to prevent their access.
Women's Solidarity and Social Media: Sisterhood Concept in #LasRespondonas, a Facebook group in Peru
Venue: arXiv cs.CY 2021
Year: 2021

Women in Peru are exposed daily to gender violence and exclusion. Several feminist groups have used social media to share information, debate, denounce, organize, and provide help to victims. This contribution analyzes the meaning of female solidarity, sisterhood or sororidad, as a feminist political concept among members of one feminist Facebook group. We reviewed the how various feminist and sisterhood concepts clustered together in the group communications by reviewing group publications and comments as far as interviewing key former and current members. The sisterhood concept was found to be central to feminist practice.
"Sharing Wisdoms from the East": Developing a Native Theory of ICT4D Using Grounded Theory Methodology (GTM) -- Experience from Timor-Leste
Venue: arXiv cs.CY 2021
Year: 2021

There have been repeated calls made for theory-building studies in ICT4D research to solidify the existence of this research field. However, theory-building studies are not yet common, even though ICT4D as a research domain is a promising venue to develop native and indigenous theories. To this end, this paper outlines a theory-building study in ICT4D, based on the author's experience in developing a mid-range theory called 'Cultivating-Sustainability' of E-government projects, a native mid-range theory of ICT4D. The paper synthesizes the GTM literature and provides a step-by-step illustration of GTM use in practice for research students and early career ICT4D academics. It introduces the key strategies and principles of GTM, such as the theoretical sampling strategy, the constant comparison strategy, the concept-emergent principle, and the use of literature throughout the study process. Then discusses the steps involved in the data collection and analysis process to develop a theory using case studies as sources of empirical data; it concludes with a discussion on using the strategies and principles in the three case studies. It is expected that this paper contributes to the diversification of research methodology, particularly to our collective quest for developing native and indigenous theories in the ICT4D research domain.
Designing Mobile Health for User Engagement: The Importance of Socio-Technical Approach
Venue: arXiv cs.CY 2021
Year: 2021

Despite the significance of user engagement for efficacy of mobile health (mHealth) in the Global South, many such interventions do not include user-engaging attributes. This is because socio-technical aspects are frequently not considered during the design, development, and implementation, stages of such initiatives. In addition, there is little discussion in the literature about the role socio-technical factors play in user-centered design processes for mHealth. This research posits consideration of socio-technical factors is required as techno-centric approaches to mHealth design and user engagement, as well as those relying on existing universal frameworks for user-centered design, have proven to be ineffective with the result that most mHealth projects in the Global South fail to sustain. This research examines projects in Sierra Leone where semi-structured interviews were conducted with mHealth designers and developers in order to explore their attitudes towards user engagement in this case. Barriers and facilitators to user engagement were identified and classified as either technical or socio-technical. Findings from the study indicate that adoption of a techno-centric approach without consideration of socio-technical factors can negatively affect user's engagement. Based on these findings, we propose to develop a new design framework for more effective inclusion of user-engaging attributes in mHealth.
Gendering of Smartphone Ownership and Autonomy among Youth: Narratives from Rural India
Venue: arXiv cs.CY 2021
Year: 2021

This study delves into the research question: how does gender influence smartphone ownership and autonomy in using the internet among the youth in rural India? This paper explores the influence of local culture on smartphone ownership and autonomy through an ethnographic study among rural Indian youth by analysing the intersection of gender with other identity axes. The findings show that young people's smartphone ownership and autonomy is shaped by their social and cultural setting, and could lead to various inequalities in their internet usage. This study shows that gender paves way for various disparities with regard to smartphone ownership and internet usage. Decolonisation of the understanding of smartphone ownership and internet usage patterns of the youth in the Global South suggests a reconsideration of the user experience designs and platform policies.
Survey of Cyber Violence Against Women in Malawi
Venue: arXiv cs.CY 2021
Year: 2021

The purpose of this study was to investigate the prevalence of cyber violence against women in Karonga district of Malawi. The study adopted a descriptive survey design. About 67 women participated in the survey questionnaire. The study utilised Technology Facilitated Gender-Based Violence Framework as theoretical lens. The study noted that women experienced various forms of cyber violence such as cyber bullying, cyber harassment, online defamation, cyberstalking, sexual exploitation, online hate speech, and revenge pornography. Perpetrators used digital platforms such Facebook, WhatsApp, online personal accounts, dating sites, and smartphones to carry out their evil acts. The study also found that perpetrators' motivations were driven by revenge, anger, jealousy, sexual desire and political agenda, with the intentions to harm the victims socially, psychologically, economically, and physically. The women used coping measures such as confrontation and blocking the perpetrator or by leaving the online platform. It was found that women never bothered to report the incidences to the police or community to seek for support due to lack of awareness, cultural and patriarchal factors. In conclusion, the study found that the prevalence of cyber violence against women is rising significantly in Malawi. Therefore, the findings provide insights to policy makers and research practioners on how to implement strategies for combating cyber violence against women in the country.
CoMet: Modeling Group Cohesion for Socially Compliant Robot Navigation in Crowded Scenes
Venue: arXiv cs.RO 2021
Year: 2021

We present CoMet, a novel approach for computing a group's cohesion and using that to improve a robot's navigation in crowded scenes. Our approach uses a novel cohesion-metric that builds on prior work in social psychology. We compute this metric by utilizing various visual features of pedestrians from an RGB-D camera on-board a robot. Specifically, we detect characteristics corresponding to proximity between people, their relative walking speeds, the group size, and interactions between group members. We use our cohesion-metric to design and improve a navigation scheme that accounts for different levels of group cohesion while a robot moves through a crowd. We evaluate the precision and recall of our cohesion-metric based on perceptual evaluations. We highlight the performance of our social navigation algorithm on a Turtlebot robot and demonstrate its benefits in terms of multiple metrics: freezing rate (57% decrease), deviation (35.7% decrease), and path length of the trajectory(23.2% decrease).
Data Augmentation Using Many-To-Many RNNs for Session-Aware Recommender Systems
Venue: arXiv cs.IR 2021
Year: 2021

The ACM WSDM WebTour 2021 Challenge organized by Booking.com focuses on applying Session-Aware recommender systems in the travel domain. Given a sequence of travel bookings in a user trip, we look to recommend the user's next destination. To handle the large dimensionality of the output's space, we propose a many-to-many RNN model, predicting the next destination chosen by the user at every sequence step as opposed to only the final one. We show how this is a computationally efficient alternative to doing data augmentation in a many-to-one RNN, where we consider every subsequence of a session starting from the first element. Our solution achieved 4th place in the final leaderboard, with an accuracy@4 of 0.5566.
Visualizing Uncertainty in Probabilistic Graphs with Network Hypothetical Outcome Plots (NetHOPs)
Venue: arXiv cs.HC 2021
Year: 2021

Probabilistic graphs are challenging to visualize using the traditional node-link diagram. Encoding edge probability using visual variables like width or fuzziness makes it difficult for users of static network visualizations to estimate network statistics like densities, isolates, path lengths, or clustering under uncertainty. We introduce Network Hypothetical Outcome Plots (NetHOPs), a visualization technique that animates a sequence of network realizations sampled from a network distribution defined by probabilistic edges. NetHOPs employ an aggregation and anchoring algorithm used in dynamic and longitudinal graph drawing to parameterize layout stability for uncertainty estimation. We present a community matching algorithm to enable visualizing the uncertainty of cluster membership and community occurrence. We describe the results of a study in which 51 network experts used NetHOPs to complete a set of common visual analysis tasks and reported how they perceived network structures and properties subject to uncertainty. Participants' estimates fell, on average, within 11% of the ground truth statistics, suggesting NetHOPs can be a reasonable approach for enabling network analysts to reason about multiple properties under uncertainty. Participants appeared to articulate the distribution of network statistics slightly more accurately when they could manipulate the layout anchoring and the animation speed. Based on these findings, we synthesize design recommendations for developing and using animated visualizations for probabilistic networks.
On Quantifying Literals in Boolean Logic and Its Applications to Explainable AI
Venue: arXiv cs.AI 2021
Year: 2021

Quantified Boolean logic results from adding operators to Boolean logic for existentially and universally quantifying variables. This extends the reach of Boolean logic by enabling a variety of applications that have been explored over the decades. The existential quantification of literals (variable states) and its applications have also been studied in the literature. In this paper, we complement this by studying universal literal quantification and its applications, particularly to explainable AI. We also provide a novel semantics for quantification, discuss the interplay between variable/literal and existential/universal quantification. We further identify some classes of Boolean formulas and circuits on which quantification can be done efficiently. Literal quantification is more fine-grained than variable quantification as the latter can be defined in terms of the former. This leads to a refinement of quantified Boolean logic with literal quantification as its primitive.
Subject Envelope based Multitype Reconstruction Algorithm of Speech Samples of Parkinson's Disease
Venue: arXiv cs.SD 2021
Year: 2021

The risk of Parkinson's disease (PD) is extremely serious, and PD speech recognition is an effective method of diagnosis nowadays. However, due to the influence of the disease stage, corpus, and other factors on data collection, the ability of every samples within one subject to reflect the status of PD vary. No samples are useless totally, and not samples are 100% perfect. This characteristic means that it is not suitable just to remove some samples or keep some samples. It is necessary to consider the sample transformation for obtaining high quality new samples. Unfortunately, existing PD speech recognition methods focus mainly on feature learning and classifier design rather than sample learning, and few methods consider the sample transformation. To solve the problem above, a PD speech sample transformation algorithm based on multitype reconstruction operators is proposed in this paper. The algorithm is divided into four major steps. Three types of reconstruction operators are designed in the algorithm: types A, B and C. Concerning the type A operator, the original dataset is directly reconstructed by designing a linear transformation to obtain the first dataset. The type B operator is designed for clustering and linear transformation of the dataset to obtain the second new dataset. The third operator, namely, the type C operator, reconstructs the dataset by clustering and convolution to obtain the third dataset. Finally, the base classifier is trained based on the three new datasets, and then the classification results are fused by decision weighting. In the experimental section, two representative PD speech datasets are used for verification. The results show that the proposed algorithm is effective. Compared with other algorithms, the proposed algorithm achieves apparent improvements in terms of classification accuracy.
SegMix: Co-occurrence Driven Mixup for Semantic Segmentation and Adversarial Robustness
Venue: arXiv cs.CV 2021
Year: 2021

In this paper, we present a strategy for training convolutional neural networks to effectively resolve interference arising from competing hypotheses relating to inter-categorical information throughout the network. The premise is based on the notion of feature binding, which is defined as the process by which activations spread across space and layers in the network are successfully integrated to arrive at a correct inference decision. In our work, this is accomplished for the task of dense image labelling by blending images based on (i) categorical clustering or (ii) the co-occurrence likelihood of categories. We then train a feature binding network which simultaneously segments and separates the blended images. Subsequent feature denoising to suppress noisy activations reveals additional desirable properties and high degrees of successful predictions. Through this process, we reveal a general mechanism, distinct from any prior methods, for boosting the performance of the base segmentation and saliency network while simultaneously increasing robustness to adversarial attacks.
Modeling Dynamics of Facial Behavior for Mental Health Assessment
Venue: arXiv cs.CV 2021
Year: 2021

Facial action unit (FAU) intensities are popular descriptors for the analysis of facial behavior. However, FAUs are sparsely represented when only a few are activated at a time. In this study, we explore the possibility of representing the dynamics of facial expressions by adopting algorithms used for word representation in natural language processing. Specifically, we perform clustering on a large dataset of temporal facial expressions with 5.3M frames before applying the Global Vector representation (GloVe) algorithm to learn the embeddings of the facial clusters. We evaluate the usefulness of our learned representations on two downstream tasks: schizophrenia symptom estimation and depression severity regression. These experimental results show the potential effectiveness of our approach for improving the assessment of mental health symptoms over baseline models that use FAU intensities alone.
Patterns of ICT usage in disaster in Samoa
Venue: arXiv cs.CY 2021
Year: 2021

The study discussed in this paper focuses on ICT use during disasters in Samoa and is a replicate of a study carried out in 2015. The study used a survey to explore how Samoan citizens use technology, act on different types of information, and how the information source or media affects decisions to act during a disaster. Findings revealed that traditional broadcasting were still the most prominent, most important, and still predominate in early warning and disaster response. However, there were now increasing usage of mobile and social media in disaster communications. Findings also revealed that people trust official reporters the most as source of information in times of crisis. The intent is that findings from this study can contribute to a people-centred approach to early warning and disaster providing empowerment to affected individuals to act in a timely and appropriate manner to ensure survival in times of disaster.
Factors that Determine Continuous Intention to Use Mobile Payments in Malawi
Venue: arXiv cs.CY 2021
Year: 2021

The proliferation of mobile phones has made mobile payments to be widely used in developing economies. However, mobile payment usage in Malawi is low, and there are many limitations to encourage users to continuously use mobile payments. The purpose of this research was to examine determinants of continuous intention to use mobile payments in Malawi. A conceptual framework adapted from Technology Acceptance Model was developed. Data was collected through a survey while data analysis used Structural Equation Modelling Partial Least Squares using SmartPLS software. The findings of this study showed that society norms significantly influence continuous intention to use mobile payments (p=0.012). Most interestingly, prior knowledge (p=0.000) and seamlessness (p=0.000) had the strongest influence as compared to structural assurance (p=0.008). Seamlessness significantly influenced satisfaction (p=0.002) and society norms (p=0.001). Seamlessness and service quality had significantly negative effects on satisfaction. The findings of this research provide several considerations to guide the mobile payments industry in Malawi. The findings may also improve the existing mobile payments system's business models, marketing strategies, customer engagement on security issues, transparency, and interoperability of payment systems. Regulators may also find the findings of this study very insightful in advancing the mobile payments agenda in Malawi.
Pull Request Latency Explained: An Empirical Overview
Venue: arXiv cs.SE 2021
Year: 2021

Pull request latency evaluation is an essential application of effort evaluation in the pull-based development scenario. It can help the reviewers sort the pull request queue, remind developers about the review processing time, speed up the review process and accelerate software development. There is a lack of work that systematically organizes the factors that affect pull request latency. Also, there is no related work discussing the differences and variations in characteristics in different scenarios and contexts. In this paper, we collected relevant factors through a literature review approach. Then we assessed their relative importance in five scenarios and six different contexts using the mixed-effects linear regression model. We find that the relative importance of factors differs in different scenarios, e.g., the first response time of the reviewer is most important when there exist comments. Meanwhile, the number of commits in a pull request has a more significant impact on pull request latency when closing than submitting due to changes in contributions brought about by the review process.